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This study examines a two-stage two-dimensional cutting stock problem encountered by a paper mill company. The problem includes various machine-related and operational constraints based on real-world situations. Paper products are manufactured using two major cutting processes. Each cutting machine has a specific minimum and maximum width for input and output rolls and is limited by the maximum number of rolls it can cut at the same time. A mathematical model is presented to formally address the problem and an efficient multiple-choice knapsack-based heuristic algorithm is proposed to solve the problem. To demonstrate the efficiency of the proposed heuristic algorithm, computational experiments are conducted on test data-set generated from real-world data provided by a large paper mill company in the Republic of Korea. [ABSTRACT FROM PUBLISHER]

The aim of this article is to develop an experiment and a procedure to investigate the restoration of water-damaged paper and archival materials using freeze drying in order to allow a reproducible test and comparison of the influence of different operating conditions on drying time and restored paper quality. Firstly, a reproducible method for the preparation of soaked samples simulating water-damaged paper has been developed. Then, the samples have been freeze-dried in a laboratory-scale apparatus that allowed monitoring the temperature as well as the weight of the samples. The technique of evaporative freezing, which reduces the drying time required, has been used in this case. An innovative procedure for the visualization of the progress of the drying process has been validated, thus allowing the validation of a simple phenomenological model of the time evolution of the ice core volume; in addition, data on the residual moisture of the dried paper sheets in different zones have been given. Finally, optimization of this particular drying process by using simple or more sophisticated approaches has been discussed. [ABSTRACT FROM AUTHOR]

There are usually many sources for the supply of raw material to a pulp or paper mill in Sweden. Optimization of this supply is therefore a challenging task, and can only be managed properly if all aspects of risk are considered. In our study, these risks are related to when the weather reduces the load-bearing capacity of the ground or the roads. A stochastic and a deterministic model have been formulated, and they have been solved with mixed-integer quadratic programming and tested with data from a Swedish forest company. The results of this study show that the option value is greater than zero and that both the optimal policy and the option value change whenever the storage cost is altered. This shows that the optimal planning policy obtained from the stochastic model differs from the solution of the deterministic model. [ABSTRACT FROM AUTHOR]

The article discusses the result of an experimental study which shows that people do not follow the optimal strategy in practice and suggests two strategies for defeating human opponents in rock-paper-scissors (RPS). The study found that the probability of repeating a game on the next trial is well above one-third, while the strategy of always playing the symbol is assessed to be an effective strategy in defeating the symbol that the other player has previously played. The study also observed that the wins of each competition have increased by 26.4%, while the losses are decreased by 9.51%.

This paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs. [ABSTRACT FROM AUTHOR]

A non-smooth biobjective optimization problem for designing the shape of a slice channel in a paper machine headbox is described. The conflicting goals defining the optimization problem are the ones determining important quality properties of produced paper: 1) basis weight should be even and 2) the wood fibers of paper should mainly be oriented to the machine direction across the width of the whole paper machine. The novelty of the considered approach is that maximum deviations are used instead of least squares when objective functions are formed. For the solution of this problem, a multiobjective genetic algorithm based on nondominated sorting is considered. The numerical results demonstrate the ability to obtain a large set of nondominated designs. [ABSTRACT FROM AUTHOR]

This paper offers an in-depth examination of the ergonomics of human-centred assembly systems in Industry 4.0, where manual tasks remain essential. The use of advanced technologies such as motion capture (MOCAP) and virtual reality (VR) is analysed as ways to enhance system efficiency and improve worker well-being. The paper highlights the importance of optimising assembly system performance while considering both economic and human factors. Metrics to assess ergonomic risk and productivity are discussed based on human-centred technologies, and existing operational research models are explored to analyse how human factors could be considered in optimising system performance. Additionally, the paper explores potential future directions and how they could play a role in Industry 4.0. [ABSTRACT FROM AUTHOR]

Box's paper helicopter has been used to teach experimental design for more than a decade. It is simple, inexpensive, and provides real data for an involved, multifactor experiment. Unfortunately it can also further an all-too-common practice that Professor Box himself has repeatedly cautioned against, namely ignoring the fundamental science while rushing to solve problems that may not be sufficiently understood. Often this slighting of the science so as to get on with the statistics is justified by referring to Box's oft-quoted maxim that "All models are wrong, however some are useful." Nevertheless, what is equally true, to paraphrase both Professor Box and George Orwell, is that "All models are wrong, but some are more wrong than others." To experiment effectively it is necessary to understand the relevant science so as to distinguish between what is usefully wrong, and what is dangerously wrong. This article presents an improved analysis of Box's helicopter problem relying on statistical and engineering knowledge and shows that this leads to an enhanced paper helicopter, requiring fewer experimental trails and achieving superior performance. In fact, of the 20 experimental trials run for validation–10 each of the proposed aerodynamic design and the conventional full factorial optimum–the longest 10 flight times all belong to the aerodynamic optimum, while the shortest 10 all belong to the conventional full factorial optimum. I further discuss how ancillary engineering knowledge can be incorporated into thinking about–and teaching–experimental design. [ABSTRACT FROM AUTHOR]

This paper solves the problem of designing positive functional $ H_{\infty } $ H ∞ filters for positive linear time-delay systems. In fact, we propose a new positive reduced order $ H_{\infty } $ H ∞ filter for positive linear constant state time-delay systems for which the states remain in the nonnegative orthant of the state space, subject to disturbances and unknown inputs. This paper is a first attempt to design positive unknown inputs functional $ H_{\infty } $ H ∞ filter for such positive linear delayed systems. The proposed approach is based on the positivity of an augmented system composed of dynamics of both considered system and proposed filter, on the unbiasedness of the estimation error by the resolution of Sylvester equation and also on Lyapunov-Krasovskii stability theory; then conditions for the establishment of such filters are formulated in terms of an optimization problem. An algorithm that summarizes the different steps of the proposed positive functional $ H_{\infty } $ H ∞ filter design is given. Finally, numerical example and simulation results are given to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

Their work is an extension of algorithms devised for equilibrium problems, proposed by Moudafi, and by Iusem and Sosa, to the more general context of quasi-equilibrium problems. We are very proud to present the special issue of Optimization related to the XIII Brazilian Workshop on Continuous Optimization, held in September 2019, celebrating Alfredo Iusem's 70th anniversary. Pedro Jorge S. Santos, Paulo Sérgio M. Santos, and Susana Scheimberg contributed their paper I A Newton-type method for Quasi-Equilibrium Problems and applications i , where they extend a Newton-type method for particular Equilibrium Problems to the setting of Quasi-Equilibrium Problems. [Extracted from the article]

For distribution networks with fuzzy network structure or large scale, traditional reliability assessment is limited by data collection and lack of data samples. Compared with traditional methods, the distribution network reliability prediction method can use fewer data to calculate and obtain reliability results, and its operation is simpler and more practical. In this paper, a distribution network original parameters and reliability prediction method based on wavelet neural network (WNN) and quantum particle swarm optimization algorithm (QPSO) is proposed. Firstly, given the blindness of mother wavelet selection, this paper analyses the error and running time through example analysis and selects the most suitable mother wavelet for distribution network reliability prediction. According to the characteristics of premature convergence of QPSO, the evolutionary speed factor and aggregation factor are introduced to modify the scaling factor to control the convergence of the algorithm. The improved QPSO is used to optimize the initial values and thresholds of the WNN. It can reduce their influence on the prediction results. Finally, the analysis results of different examples show that the method has higher forecast accuracy, better generalization ability, and stability. This method also provides new scientific ideas for the reliability prediction of distribution networks. [ABSTRACT FROM AUTHOR]

In this article, a bi-level optimization problem covering upper (design) and lower (operation) levels is defined and a solution procedure for bi-level optimization problems is presented. This is devised as a dynamic multiobjective optimization problem, i.e. the values of the control and state variables change over a predefined time horizon and several competing criteria are optimized simultaneously. Moreover, the interaction between the upper and lower levels is analysed. The benefits of bi-level dynamic multiobjective optimization are illustrated in detail by examining an industrial case in which the design of a paper mill (upper level) and the mill operation (lower level) are optimized at the same time. However, the problem definition and the solution procedure are not limited to any specific application but can be exploited in many different industrial areas. [ABSTRACT FROM PUBLISHER]

This paper proposes a scheduling strategy for irregular curved blocks to address the complex spatiotemporal coupling scheduling problem related to the entered time, the entered sequence, the setting positions and the rotated angles for the curved blocks in a shipbuilding yard. The strategy presents a makespan-based curved blocks - classification and selection rule to fulfil the programming time for the entry of the curved blocks into the workplace and realises the suppression on the delay. Useless stepping search of curved blocks in occupied workplace is avoided by combining the lowest centre-of-gravity rule with the calculation method of the remained workplace proposed in this paper. A modified genetic ant colony algorithm was proposed, which apply the ease to premature characteristics of GA and the excellent local optimisation ability of ACO, to let and promote the algorithm falls into local optimum. Then the large-scale and full-range mutation will be implemented to make the algorithm jump out of the original local optimisation to search more local optimal solutions so that the global optimal solution can be achieved. Finally, a software system for algorithm verification was developed which conducts the comparative analysis of the algorithms and verifies the validity of the algorithm proposed. [ABSTRACT FROM AUTHOR]

With the increasing demand for multi-variety and small-batch products, it's necessary to frequently dispatch and replace the progressive press dies on the stamping production lines to ensure the diversity of processed automobile covering parts. This paper formulates a die scheduling problem with twin-crane transportation (DSP-TCT) encountered in the stamping production line, which concentrates on the scheduling of transporting dies between the production line and warehouse by twin cranes with satisfying crane distance constraint, die position constraint, and precedence constraint. To solve DSP-TCT, this paper proposes a node sequence-based ant colony optimisation algorithm (NS-ACO). In this algorithm, each node represents a single die transportation task with action and time information executed by the twin cranes. The combination of adjacent nodes with a high time utilisation rate can be accumulated as heuristic priority knowledge for guiding optimisation. To demonstrate the effectiveness of the NS-ACO algorithm, numerical experiments with three different die stacking strategies are executed. [ABSTRACT FROM AUTHOR]

Multi-resource constrained dynamic workshop scheduling is a complex and challenging task in discrete manufacturing. In this paper, to obtain a high-performance scheduling in limited time, this problem is modelled into a Markov decision process, and solved by proximal policy optimisation algorithm, which can learn from the simulated workshop environment directly. A multi-modal hybrid neural network is used in the model to make good use of numerical state features representing workshop environment information and graphical state features representing constraint information during the learning process. Multi-label technique is used in this paper to decouple the output acts of jobs, machines, tools, and workers. Action mask technique coding the constraints is also used to prune invalid exploration. The experimental results show that compared with heuristic rules such as weighted shortest processing time, weighted modified due date, weighted cost over time, apparent tardiness cost and other reinforcement learning methods such as DeepRM and DeepRM2, the performance of the proposed method is at least 1.138 % better in scheduling penalty. [ABSTRACT FROM AUTHOR]

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations' costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Operational decisions are influenced not only by the data and models available to the decision maker but also by the uncertainty in the data and in model-based predictions about the impacts of decision makers' actions. In non-linear systems the potential actions may have widely differing uncertainty associated with them. Then the decision maker must take an attitude towards risk and balance that against the expectation value of performance. In stochastic optimization, methods to deal with uncertainty have been developed. However, these methods have not been widely used in decision making about operating industrial processes. In this article, we first present a short summary of decision making under uncertainty and then suggest that the mathematical structure of stochastic optimization serves as a model for the architecture of future operational decision support systems. We demonstrate this framework by analysing four idealized operational decision cases, which are closely related to practical daily decision making tasks at paper mills. However, the explicit risk analysis introduces concepts that are new to operational decision makers - operators and engineers - and thus is challenging to implement in practice. [ABSTRACT FROM AUTHOR]

Energy cost optimisation in manufacturing systems has gained more and more attention. Although there are many papers about energy consumption optimisation in serial production lines, energy cost optimisation in serial production lines has rarely been focused. In this paper, we formulate an energy cost optimisation problem in two-machine Bernoulli serial line under time-of-use pricing. We analyse the structural characteristics of the problem and transform the problem into optimally allocating the production rate among the time periods of different electricity rates. A definition of the extreme allocation is proposed and completed, and the optimal allocation is proved to be one of the extreme allocations. Using the property, an efficient method to solve the optimal allocation is proposed. With the help of the method, the multi-electricity-rate problem is transformed into several single-electricity-rate problems, which has been solved in the literature. [ABSTRACT FROM AUTHOR]

The Physical Internet (PI) logistics system is an innovative logistics concept that has been gathering a lot of attention lately. This system consists of open, modular and shared containers and transit hubs to move goods globally. The purpose of this paper is to compare the performance of PI with regard to the conventional (CO) logistics system in order to quantify the advantages and disadvantages of PI from a truck and driver routing perspective with an explicit constraint on maximum return time for drivers. The comparison presented in this work is carried out through Monte-Carlo simulation within a sequential three-phase optimisation framework. Based on our analysis, PI reduces driving distance (and time), GHG (greenhouse gas) emissions and the social cost of truck driving. On the other hand, it increases the number of container transfers within the PI logistics centres. This insight is a contribution of the paper and reinforces the current literature on PI. The other main contribution of the paper is a validation of the claim that the number of drivers who can go back home at the end of a work day remains consistently high in PI, regardless of the traffic level. [ABSTRACT FROM PUBLISHER]

This paper proposes an automated scanning process of a structured light system for objects without overhangs. The processes for scanning those objects need to plan scanning directions that minimise the missing area on a three-dimensional surface during the scanning process. Thus, the processes require an approach that finds the next scanning direction efficiently in terms of computational costs. This paper develops a scanning simulation approach to meet this requirement. In order to apply the developed approach, the proposed process generates asolution spacefor candidate-scanning directions, and represents an intermediate 3D model. The developed approach traverses the solution space in a virtual environment and executes virtual scanning for the intermediate 3D model. The virtual scanning result of each candidate-scanning direction is analysed in order to evaluate the contribution for filling missing area. The proposed process defines key scanning directions in the solution space through the iterative execution of the developed approach. The proposed process has been implemented, and applied to the scanning experiments of dental impressions. [ABSTRACT FROM PUBLISHER]

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

Good Laboratory Practice has been a part of non-clinical research for over 40 years. Optimization Research, despite having many papers discussing standards being published over the same period of time, has yet to embrace standards that underpin its research. In this paper we argue the need to adopt standards in optimization research. Building on previous papers, many of which have suggested that the optimization research community should adopt certain standards, we suggest a concrete set of recommendations that the community should adopt. We also discuss how the proposals in this paper could be progressed. [ABSTRACT FROM AUTHOR]

A new multi gain SC inverter topology with fewer switching components and lower voltage stress is presented in this paper. It comprises eight switches and two capacitors, which raise the output voltage by three times the supply voltage. The capacitors voltages are automatically balanced to ensure proper operation. The circuit constructional, operation, and parameter optimisation techniques are discussed in details. A simple logic-based multicarrier Level Shift Pulse Width Modulation (LS-PWM) is employed to control the switching operation that reduces the complexity of the control circuit. Furthermore, a comparative analysis with other recent topologies has been presented to demonstrate the virtues of this approach. Finally, the proposed topology (PT) verification, feasibility, and effectiveness have been demonstrated both in simulation and experimentation. [ABSTRACT FROM AUTHOR]

Operations Research (OR) is a branch of applied mathematics that deals with optimization problems arising from different real contexts. The solving process of its problems is based on the construction and resolution of mathematical models, showing the possible connections between mathematics and the real world. Nevertheless, OR is not typically included in most curricula of higher secondary schools (i.e. Grades 9–12), but it is usually presented mainly at university level. To show how OR could be significant for these school students' education, the authors of this paper developed an educational project consisting of three teaching units. In this paper, we share the result of the teaching experiment related to the first unit, addressed to Grade 10. Qualitative and quantitative analyses show how it is appropriate to include OR and its typology of problems in regular school mathematics lectures. Second, these data also show how modelling and problem-solving skills, developed working with OR, can be fostered by implementing a collaborative way of working, also by making use of digital technologies. Last but not least, we demonstrate the positive impact such activities have on students' appreciation of OR. [ABSTRACT FROM AUTHOR]

This paper makes a game-theoretic analysis of an M/M/1 queue with variable vacation and vacation interruptions. The Nash equilibrium mixed strategy and social utility maximization are derived based on a non-cooperative game theory and an optimization theory under different information precision levels, namely almost unobservable and fully unobservable cases. The explicit solutions of the entrance probabilities and the steady-state probability distribution of the system are investigated using the probability generating function and nonhomogeneous linear difference equations. Furthermore, the effects of the information levels and diverse system parameters on the equilibrium strategies, the arrival rates, and the expected benefits are explicitly illustrated by numerical comparisons. The research results can provide a theoretical basis and performance analysis tool for the optimal design in the wireless transmission and network communication system. [ABSTRACT FROM AUTHOR]

The efficient design of the aerodynamic surfaces in the aircraft allows the optimal performance of the vehicle and the reduction of the fuel consumption. Among these surfaces, the wing is the main contributor to the force which lifts the aircraft enabling the flight. However, the application of this force out of the center of gravity generates a moment that must be balanced through a force applied in the Horizontal-Tail-Plane (HTP) reaching the longitudinal trim of the aircraft. Traditionally, the design of the wing and HTP have been performed iteratively attaining suboptimal or time ineffective results. In this paper, we solve this problem through the application of a Genetic Algorithm for the combined optimization of the wing and the HTP by adjusting the aspect ratio, the taper ratio, the twist angle and the incidence angle of both surfaces to produce the optimal balance of lift adjusting its distribution to an elliptical configuration and enabling the longitudinal trim. Results show the automatic adjustment of the parameters of the aerodynamic surfaces thus fulfilling the objectives of this paper. [ABSTRACT FROM AUTHOR]

A dynamic programming formulation was proposed in the paper entitled ‘A new dynamic programming formulation of (n × m) flowshop sequencing problems with due dates’ [Sonmez, A.I. and Baykasoglu, A., 1998, International Journal of Production Research, 36 (8), 2269–2283] to deal with a flow shop problem considering sequence-dependent setup times to minimise the total weighted tardiness. Since the original dynamic programming formulation is confusing and of the six examples given in the original paper four are partially or totally wrong, both the dynamic programming formulation and the examples need to be rectified. In this note, the confusing interpretations of the original formulation are analysed, and a more accurate dynamic programming formulation is proposed. Based on the new dynamic programming formulation, all four incorrect examples are recalculated. [ABSTRACT FROM PUBLISHER]

The concept of reconfigurable manufacturing systems (RMSs) is a current subject that has attracted intensive research. This latter covers the entire RMS life cycle, from the design to the exploitation phase, and includes several important problems requiring the use of optimisation. The objective of this paper is to survey research publications related to RMS optimisation problems and their solution methods. For this, the types of RMS and their components are described. Subsequently, relevant objective functions and performance indicators of RMS are presented. In addition, an overview of the most used solution approaches and a classification of optimisation problems are proposed. Finally, a detailed analysis, our conclusions, and suggestions for future research are provided. [ABSTRACT FROM AUTHOR]

Over the years, several achievements have been made in power generation and optimising hybrid renewable energy systems (HRES) to achieve nature conservation, achieve energy security, and reduce carbon emissions. However, there are many complexities in Renewable energy (RE) conversion, sizing, design, and implementation, that require optimisation techniques to achieve optimal results in terms of reliability, cost, and environmental protection over time. This paper presents an overview of research trends in Optimization methods in HRES which are classified into modern and conventional methods. These two classifications are further divided into control methods, Artificial intelligence, Iterative and mathematical operations. However, all mentioned techniques have inherent advantages and disadvantages which will be discussed in this survey. In addition, the review paper explored different types of research in computing intelligence (CI), an aspect of Artificial Intelligence (AI) that involves the development of nature-inspired algorithms for optimisation. Finally, general optimisation criteria, system sizing methods used in RES, Mathematical modelling of RES, and gaps for future work to achieve sustainability were also presented. [ABSTRACT FROM AUTHOR]

Agent-based evolutionary, computational systems have been proven to be an efficient concept for solving complex computational problems. This paper is an extension of [Biełaszek, S., Piętak, K., & Kisiel-Dorohinicki, M. (2021). New extensions of reproduction operators in solving LABS problem using EMAS meta-heuristic. Springer, cop. 2021. – Lecture Notes in Artificial Intelligence, Computational collective intelligence 12876 304-316. 13th International Conference, ICCCI 2021: Rhodes, Greece, September 29ŰOctober 1, 2021.] where we proposed new variants of reproduction operators together with new heuristics for the generation of initial population, dedicated to LABS – a hard discrete optimization problem. In this research, we verify if the proposed recombination operators improve EMAS efficiency also with different local optimization techniques such as Tabu Search and Self-avoiding walk, and therefore can be seen as better recombination operators dedicated to LABS problem in general. This paper recalls the definition of new recombination variants dedicated to LABS and verify if they can be successfully used in many different evolutionary configurations. [ABSTRACT FROM AUTHOR]

In this paper, critical analysis of performance comparison of various optimization techniques for a MRPID (Multi Resolution Proportional Integral Derivative) thermal system controller is presented. This controller controls the uncertainty or mismatch between real and reference temperature. Wavelet coefficients of error between the two temperatures and their corresponding gains are added to produce a control signal for the thermal system. The main purpose of the proposed method is to optimize the tuning parameters of a wavelet-based MRPID controller to regulate the switching pulse of the thermal system. Modeling and simulations are carried out using MATLAB/Simulink@2015. Figures-of-merit such as disturbance rejection, system stability and transient response are also compared using various techniques like Particle swarm optimization (PSO)-MRPID, Genetic Algorithm (GA)-MRPID (existing techniques), Firefly-MRPID controllers and Elephant herding optimization (EHO) based Recurrent neural network (RNN)-MRPID algorithm. Firefly-MRPID and EHO with RNN-based MRPID are proposed for determining unique (best) operating conditions of the thermal system in this paper. [ABSTRACT FROM AUTHOR]

Methods for optimization of islanded microgrid systems are usually based on hourly models where each subcomponent is described by a simple algebraic model. There are many studies on this topic, which are usually based on the minimization of total lifetime cost by determining the number of required batteries, wind turbines, PV panels, the positioning of PV panels, etc. In this paper, we further improve the modeling of the microgrid system optimization process by developing a simplified algebraic model that uses one parameter to simulate accelerated battery degradation with respect to depth of discharge. The model consists of simply linearly increasing the degradation of the battery when the state of charge (SOC) becomes lower than a fixed value, and the only model constant is the factor of degradation f. The objective of the paper is to examine the effect of the degree of degradation on the obtained optimal microgrid system parameters. The analysis was performed for several different systems, and the results show that optimal parameters of the system and the overall system cost strongly depend on battery degradation characteristics. The overall system cost can be reduced by 1–6% for lower battery degradation rates and up to 20% for high degradation factor cases. Increasing the degradation factor also has an influence on the ratio of wind turbines to PV panels, and the optimal size of the battery system. [ABSTRACT FROM AUTHOR]

This paper studies the multi-item stochastic capacitated lot-sizing problem with stationary demand to minimise set-up, holding, and backorder costs. This is a common problem in the industry, concerning both inventory management and production planning. We study the applicability of the Proximal Policy Optimisation (PPO) algorithm in this problem, which is a type of Deep Reinforcement Learning (DRL). The problem is modelled as a Markov Decision Process (MDP), which can be solved to optimality in small problem instances by using Dynamic Programming. In these settings, we show that the performance of PPO approaches the optimal solution. For larger problem instances with an increasing number of products, solving to optimality is intractable, and we demonstrate that the PPO solution outperforms the benchmark solution. Several adjustments to the standard PPO algorithm are implemented to make it more scalable to larger problem instances. We show the linear growth in computation time for the algorithm, and present a method for explaining the outcomes of the algorithm. We suggest future research directions that could improve the scalability and explainability of the PPO algorithm. [ABSTRACT FROM AUTHOR]

Lot streaming is the most widely used technique to facilitate the overlap of successive operations. Considering the consistent sublots and machine breakdown, this study investigates the multi-objective hybrid flowshop rescheduling problem with consistent sublots (MOHFRP_CS), which aims at optimising the total completion time, starting time deviations of operations, and average adjustment of sublot sizes simultaneously. By introducing the decomposition strategy and effective migrating birds optimisation framework, this paper develops a multi-objective migrating birds optimisation algorithm based on decomposition (MMBO/D). In MMBO/D, the problem is decomposed into a series of sub-problems, and its solutions are initialised by the Glover operator and further optimised by the variable neighbourhood descent strategy. The weights assigned to the sub-problems are adapted dynamically according to a variable weight strategy, and a global update strategy is employed to update the solutions. A novel sharing and benefiting mechanism is proposed to implement coevolution among different sub-problems. Competitive mechanisms are modified by considering similar sub-problems to improve population quality. A criterion is designed to check whether a subproblem is stuck in the local optima. The comprehensive computational results demonstrate that MMBO/D outperforms other state-of-the-art multi-objective evolutionary algorithms (MOEAs) for the addressed problem. [ABSTRACT FROM AUTHOR]

This paper focuses on the buckling of nontypical ellipsoidal domes under uniform external pressure. According to the strength and stiffness optimisation theory, the structure of the ellipsoidal dome was optimised respectively, and two meridional profiling curves of the ellipsoidal dome were obtained. Two mass-equivalent ellipsoidal domes with variable wall thickness and one mass-equivalent ellipsoidal dome with constant wall thickness were fabricated using CNC machining, with a nominal long-axis and short-axis radius of 100 and 75 mm, respectively. The geometry, wall thickness, and buckling load of each ellipsoidal dome were measured. The agreement between numerical predictions and experimental results is good. The results indicate that the ellipsoidal dome with variable wall thickness has a relatively large load-carrying capacity. [ABSTRACT FROM AUTHOR]

Thermo-elastic systems have a wide range of applications in various engineering fields. Traditional single-objective topology optimization (TO) design of thermos-elastic structures is difficult to achieve the combined optimum of multiple properties, and the non-periodic TO design of results are complex and difficult to fabricate. This paper introduces a multi-objective TO formulation based on density for designing thermo-elastic structures with periodic constraints, aiming to overcome the aforementioned issues. This paper assesses two competing weighted objective functions, the first function corresponds to structural and thermal compliance, while the second pertains to regional temperature and global stress. To solve the optimization issue, we utilize the p-norm function with a modified coefficient for evaluating the maximum temperature and stress values. Additionally, the adjoint variable method is employed to evaluate the sensitivity of various objectives, while the method of moving asymptotes (MMA) is used to update the design variables. Then the influence of different weight coefficients and subregion numbers regarding thermos-elastic coupled analysis is demonstrated through numerical examples. The results show that the complexity of the subregion features decreases as the number of subregions increases, and that changing both the number of subregions and the weighting coefficients results in changes in the overall structural performance. Finally, the geometric model is reconstructed using the TO results, and the structural performance is validated through the simulation of the reconstructed model. The results indicate that the TO results obtained by the method of this paper have predefined performance. [ABSTRACT FROM AUTHOR]

Combinational logic circuits (CLCs) are basic building blocks of a system and optimization of these circuits in terms of reduced gates, transistors, or levels will lead to reduced area on chip, reduced power, and improved speed. Simulated annealing (SA) is a thermo-inspired metaheuristic used for solving various engineering and non-engineering problems. SA is also used for the realization and optimization of CLCs. Circuits with a large number of inputs and outputs require more generations for realization. Realization of the optimal circuit with fewer generations is desired as realization time increases with increase in the number of generations. In this paper, an attempt is made to realize circuits using population-based SA with fewer generations. SA with partitioning approach is proposed in this paper for circuits that could not be realized with fewer preset generations. To evaluate the performance of the proposed work, benchmark circuits from LGSynth'91 are considered, and it is observed that the success rate improved and realization time reduced with the proposed partitioning approach. During the evaluation, it is also observed that the gate count was reduced by 2.5–77.39% and the transistor count was reduced by 7.69–95.53% on using proposed work with fewer generations over circuits reported in the literature. [ABSTRACT FROM AUTHOR]

Panax notoginseng is a valuable herb with geographical indication, and the quality and price of P. notoginseng from different origins are very different. Therefore, this paper proposes a rapid and accurate method for identifying the origins of P. notoginseng by collecting the roots of P. notoginseng. This paper improves the whale optimization algorithm in terms of global convergence and convergence speed, introduces the Levy flight strategy and reconstructed whale synergy factor A, and applies it to the parameter optimization of support vector machines, to obtain a high-performance classification model. The improved whale optimization algorithm model identifies the origin of P. notoginseng by discriminating their terahertz spectra. Compared with the commonly used genetic algorithm and the original whale optimization algorithm, improvement in the whale optimization algorithm was able to avoid falling into local optimum solutions more effectively while having a high convergence rate. Accordingly, the improved whale optimization algorithm optimized support vector machine model obtained an overall accuracy of 98.44%, which was significantly higher than the 95.31% overall accuracy of the genetic algorithm optimized support vector machine model and the 96.88% overall accuracy of the whale optimization algorithm optimized support vector machine model. It was concluded that terahertz spectroscopy together with machine learning would be a promising technique for identifying the origins of P. notoginseng. [ABSTRACT FROM AUTHOR]

A call for papers for the Special Issue of the journal "Quantitative Finance" is presented regarding stochastic optimization to energy and financial markets to be submitted on November 29, 2013 for those with intention to submit with extended abstract, and February 28, 2014 for article submission.

Working from home becomes the norm; this trend has put added pressure on urban logistics, as large volumes of goods and services are required for domestic use. Meanwhile, public transport operators face a big challenge and trade-off due to higher labour and frequent cleaning costs, with lower passenger revenue over a longer period. Considering the collaborative urban public transport services achieve a seamless movement for both passengers and goods, and could reduce the adverse effects of the existing urban public transport systems. Therefore, a mixed-integer linear programming model introducing the concept of capacity matching is proposed to assist this collaborative urban freight service network in minimising total freight transport time at station hubs and not affecting passenger transportation in this paper. Moreover, an efficient improved optimisation algorithm based on the Artificial Bee Colony Algorithm (ABC) is designed, and the numerical examples and real cases are illustrated to demonstrate the feasibility and effectiveness of the proposed model and algorithm. The performance evaluations suggest that the coordinated operating strategy of the collaborative freight transportation system supports increasing mobility demands for freight, resulting in declining congestion levels and reducing transport emissions, while no influence in passenger transport, notably in urban areas. [ABSTRACT FROM AUTHOR]

Critical infrastructure networks, such as transport and power networks, are essential for the functioning of a society and economy. The rising transport demand increases the congestion in railway networks and thus they become more interdependent and more complex to operate. Also, an increasing number of disruptions due to system failures as well as climate changes can be expected in the future. As a consequence, many trains are cancelled and excessively delayed, and thus, many passengers are not reaching their destinations which compromises customers need for mobility. Currently, there is a rising need to quantify impacts of disruptions and the evolution of system performance. This review paper aims to set-up a field-specific definition of resilience in railway transport and gives a comprehensive, up-to-date review of railway resilience papers. The focus is on quantitative approaches. The review analyses peer-reviewed papers in Web of Science and Scopus from January 2008 to August 2019. The results show a steady increase of the number of published papers in recent years. The review classifies resilience metrics and approaches. It has been recognised that system-based metrics tend to better capture effects on transport services and transport demand. Also, mathematical optimization shows a great potential to assess and improve resilience of railway systems. Alternatively, data-driven approaches could be potentially used for detailed ex-post analysis of past disruptions. Finally, several rising future scientific topics are identified, spanning from learning from historical data, to considering interdependent critical systems and community resilience. Practitioners can also benefit from the review to understand a common terminology, recognise possible applications for assessing and designing resilient railway transport systems. [ABSTRACT FROM AUTHOR]

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Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production. [ABSTRACT FROM AUTHOR]

Exponentially weighted moving average (EWMA) control charts are well-established devices for monitoring process stability. Typically, control charts are evaluated by considering their Average Run Length (ARL), that is the expected number of observations or samples until the chart signals. Because of the limitations of an average, various papers also dealt with the run length distribution and quantiles. Going beyond these papers, we develop algorithms for and evaluate the quantile performance of EWMA control charts with variance adjusted control limits and with fast initial response features, of EWMA charts based on the sample variance, and of EWMA charts simultaneously monitoring mean and variance. Additionally, for the mean charts we consider medium, late and very late process changes and their impact on appropriately conditioned run length quantiles. It is demonstrated that considering run length quantiles can protect from constructing distorted EWMA designs while optimising theirzero-stateARL performance. The implementation of all the considered measures in the R package ‘spc’ allows any control chart user to consider EWMA schemes from the run length quantile prospective in an easy way. [ABSTRACT FROM PUBLISHER]

There are many situations in which the requirements of a standard experimental design do not fit the research requirements of the problem. Three such situations occur when the problem requires unusual resource restrictions, when there are constraints on the design region, and when a nonstandard model is expected to be required to adequately explain the response. This article provides an introduction to optimal design for these types of situations. Optimal designs are computer-generated experiments that are aimed at satisfying specific research problem requirements. We show that the optimal design approach is applicable to any design problem and necessary when there are situations involving resource constraints or nonstandard design regions or models. The mathematical formulations of several design optimality criteria are presented along with examples of optimal design applications. [ABSTRACT FROM AUTHOR]

This paper presents a time modulated far-field radiation pattern synthesis of a compact circular antenna array (CAA) by employing a stochastic optimization technique known as grey wolf optimization (GWO). The compactness of the antenna array is achieved by reducing the inter-element distances between the elements of the array which leads to the miniaturization of the antenna array size. On the other hand, an optimal far-field radiation characteristic is achieved by suppressing the side lobe level (SLL) as well as by narrowing the first null beamwidth (FNBW). A lower SLL value is necessary to avoid the interference with the system working in the same frequency, whereas, a narrow FNBW helps the antenna array to attain a high directivity. The performance of a time modulated antenna array (TMAA) depends on the rise on–off time, pulse shape as well as on the switching time duration of each element of the array which radiates at different harmonic frequencies. In this paper, the generation of radiation pattern at the fundamental/central frequency and its first two harmonic frequencies are considered for the design issue. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm to verify whether a finite game is a coset weighted potential game (WPG) without pre-knowledge on its coset weights. This algorithm can also provide a recursive method to calculate the unknown coset weights. Then we give the concept of near coset WPGs based on evolutionary equivalence between two finite games, and its algebraic verification is obtained. Finally, the application of near coset WPGs to game-theoretical optimisation of multi-agent systems is discussed, which can improve the applicability of potential games in multi-agent optimisation problems. [ABSTRACT FROM AUTHOR]

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed. [ABSTRACT FROM AUTHOR]

Manufacturing industries are constantly pressured by market demands to produce low-cost, high-quality products. Market demands tend to be complex and full of variations, especially in fast-paced industrial settings. To cope with the rising market demands, traditional manufacturing techniques are forced to develop and adapt advanced practices and methods. However, as advances in manufacturing technology grow, so does the need to implement them in an efficient and cost-effective manner. The outcome of this urgent need is a combination of traditional manufacturing tools and modern technologies, hardware or software, that transforms traditional manufacturing into intelligent manufacturing. Intelligent manufacturing systems are complex systems in which optimization methodologies are combined with sensor-based control systems. Such intelligent and optimized systems are capable of producing high-quality products at fast production rates and lower cost. Among the broad range of manufacturing processes, this paper focuses on machining and aims to detail the recent advances in the field of intelligent machining. The paper is divided into two interrelated topics, namely techniques and tools. Techniques are mainly referred to optimization techniques in intelligent machining while tools focus on smart cutting tools. The paper will detail the technological trends and significant contributions in each of these areas in chronological order. [ABSTRACT FROM AUTHOR]